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Niche GenAI specialization but broad backend and deployment requirements create moderate competition among qualified candidates.
Skills are specialized to LLMs and RAG yet transferable across industries with AI practices.
Multiple mandatory LLM, RAG, vector DB, cloud and deployment skills imply high shortlisting strictness.
Design, develop, and maintain scalable generative AI applications using LLMs, RAG pipelines, and agent-based architectures.
Build and optimize backend APIs, microservices, and vector database integration for semantic search and retrieval.
Collaborate with cross-functional teams to deploy enterprise-grade AI solutions with observability, security, and performance optimization.
Proficiency in Python and relevant frameworks for backend API and microservices development.
Experience with LLM application development using LangChain, LangGraph, LangFuse, and Amazon Bedrock integrations.
Hands-on experience with RAG pipeline components: document ingestion, chunking, embeddings, retrieval, and indexing.
Work Experience Required: Not explicitly mentioned in the JD
Experienced in architecting and deploying complex Gen AI solutions at scale within enterprise environments.
Skilled in integrating security (SSO-based authentication/authorization) and monitoring for AI applications.
Familiar with containerization (Docker) and cost-performance optimization strategies for AI workloads.